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scientific discipline. Experience in developing and running an atmospheric transport model. Experience in interpreting data. Experience with FORTRAN and Python computer languages. Proven ability to work
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are carried by electrons) and biological systems (where signals are carried by ions). This research will cover theoretical models at many scales including electron dynamics, soft-matter physics, materials
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on Experimental Study and Computational Solids Mechanics model of soft multifunctional materials such as magneto-active polymers (MAPs), Electro-active polymers (EAPs). The successful candidate will work on the
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on process development, electrode manufacture and performance assessment, but depending on the skills of the successful applicant, may also involve some aspects of modelling or data science. The post is funded
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, attack surfaces, defensive mechanisms and related topics to the safe deployment of systems contain multiple LLM and VLM powered models. You will be responsible for Developing and implementing; capability
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of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, general pre-trained transformers, prompt engineering, knowledge graphs, knowledge
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publication in highly-ranked journals. 5. Demonstrable ability to present research papers at conferences and communicate complex information to specialists and within the wider academic community 6. Experience
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. The successful applicant will be able to work collaboratively, present information on research and outcomes, communicate complex information, orally, in writing and electronically and prepare proposals
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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fluorescence-lifetime detection (Fast-FLIM) and temporal focusing. This instrument will deliver quantitative, sub-second imaging of live three-dimensional cell-culture and organoid models, advancing fundamental